EEG headbands vs caps: How many electrodes do I need to detect emotions? The case of the MUSE headband

Abstract

In the realm of emotion detection, comfort and portability play crucial roles in enhancing user experiences. However, few works study the reduction in the number of electrodes used to detect emotions, and none of them compare the location of these electrodes with a com- mercial low-cost headband. This work explores the potential of wearable EEG devices, specifically the Muse S headband, for emotion classification in terms of valence and arousal. We directly compared the Muse S, with only four electrodes, and the DEAP dataset, with 32 in a more intrusive headset. Our methodology focused on utilizing raw data and extracting four common frequency ranges. In particular, we select from DEAP the 4 electrodes similar to those in the Muse S. Additionally, we created a dataset using the Muse S, segmenting the complete video into fixed-size temporal windows. Our 4-electrodes dataset uses film clips to elicit emo- tions, classified according to the Self-Assessment Manikin. Our findings indicate that the Muse S, despite its limited electrode count, can effec- tively discriminate between high and low valence/arousal emotions with accuracy comparable to the accuracy obtained with all DEAP electrodes. The Gamma band emerged as particularly effective for valence detection. Using a Muse device and raw data, the best performance achieved a G- Mean only 1–2% lower than that of the DEAP dataset, demonstrating that comparable results can be obtained with a simplified setup. Although Muse-S did not reach DEAP in terms of outcomes, it proved to be a viable, cheaper, less intrusive alternative and adaptable for everyday use.